With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function; then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints.

利用将计算部分或全部卸载到附近服务器的好处，移动边缘计算为用户设备（UE）提供了运行计算密集型应用程序的更强大功能。但是，出现了一个严峻的挑战：如何在考虑UE性能及其电池使用限制的情况下，选择最佳的组件集进行卸载。在本文中，我们提出了一种基于能源和性能的新型高效深度学习卸载算法。基于剩余能量及其性能的最优卸载方案可以通过我们提出的算法来确定。所有这些考虑因素都被建模为成本函数。然后，训练深度学习网络以计算解决方案，通过该解决方案可以确定最佳卸载方案。